Integrating Metallurgical Mechanisms and Explainable Deep Learning Methods to Predict Phosphorus Content in Electric Arc Furnace Steelmaking for Enhanced Efficiency

Abstract

Accurate prediction of phosphorus content in molten steel at the end of the electric arc furnace (EAF) smelting process is crucial for optimizing smelting efficiency and ensuring product quality. Inaccurate predictions can result in increased resource consumption and compromised product quality. This study proposes a novel FA-MM-TabNet model, which integrates Firefly Algorithm (FA) optimization, metallurgical mechanisms (MM), and the deep learning-based TabNet algorithm to predict endpoint phosphorus content in EAF operations. The model was trained and tested on actual production datasets and demonstrated superior performance compared to competing models, achieving a mean absolute error of 0.0024, a root mean square error of 0.0034, and a hit rate of 90.82% within a ±0.005% error margin. Furthermore, an interpretability analysis using explainable artificial intelligence methods, including model-specific and SHAP analyses, confirmed the model's alignment with metallurgical principles. This enhances the model's reliability and applicability in industrial settings, contributing to more efficient steelmaking practices.